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An estimation framework to quantify railway disruption parameters

Grandhi, BS; Chaniotakis, E; Thomann, S; Laube, F; Antoniou, C; (2021) An estimation framework to quantify railway disruption parameters. IET Intelligent Transport Systems 10.1049/itr2.12095. (In press). Green open access

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Abstract

Railway network operations form complex systems. Any disruption adversely impacts the operations, causing long delays. Many studies investigate the effect of a railway incident; however, a holistic quantification is lacking. This study aims to present an estimation framework for flexible traffic management systems, which can help reduce network delays and enable dispatchers to make better-informed decisions. An incident's impact on the network is estimated by creating a sequence of models, which predict two key variables. Firstly, the incident duration is predicted, which is used to predict the second variable: total delay caused by the incident. Various influencing attributes are examined, such as weather, network and railway-related attributes. Their relationship with the response variables is studied in order to understand the incident's impact. Using incident data from the Danish Railways, machine learning models are estimated. The results show that neural networks outperform other competing models for total delay modelling, resulting in improved prediction by the estimation framework, thus giving higher accuracy than the stand-alone models in the study.

Type: Article
Title: An estimation framework to quantify railway disruption parameters
Open access status: An open access version is available from UCL Discovery
DOI: 10.1049/itr2.12095
Publisher version: https://doi.org/10.1049/itr2.12095
Language: English
Additional information: © 2021 The Authors. IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources
URI: https://discovery.ucl.ac.uk/id/eprint/10131455
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